Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

4-2022

Abstract

The prevalence of graph structures attracts a surge of investigation on graph data, enabling several downstream tasks such as multigraph classification. However, in the multi-graph setting, graphs usually follow a long-tailed distribution in terms of their sizes, i.e., the number of nodes. In particular, a large fraction of tail graphs usually have small sizes. Though recent graph neural networks (GNNs) can learn powerful graph-level representations, they treat the graphs uniformly and marginalize the tail graphs which suffer from the lack of distinguishable structures, resulting in inferior performance on tail graphs. To alleviate this concern, in this paper we propose a novel graph neural network named SOLT-GNN, to close the representational gap between the head and tail graphs from the perspective of knowledge transfer. In particular, SOLTGNN capitalizes on the co-occurrence substructures exploitation to extract the transferable patterns from head graphs. Furthermore, a novel relevance prediction function is proposed to memorize the pattern relevance derived from head graphs, in order to predict the complements for tail graphs to attain more comprehensive structures for enrichment. We conduct extensive experiments on five benchmark datasets, and demonstrate that our proposed model can outperform the state-of-the-art baselines.

Keywords

Size-oriented long-tailed distribution, graph neural networks, knowledge transfer

Discipline

Graphics and Human Computer Interfaces | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the ACM Web Conference 2022, Virtual Conference, April 25-29

First Page

1506

Last Page

1516

ISBN

9781450390965

Identifier

10.1145/3485447.3512197

Publisher

ACM

City or Country

Virtual Conference

Additional URL

http://doi.org/10.1145/3485447.3512197

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